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References (20)

[1]
CALM: A CKA-Guided Adaptive Layer-Wise Modularization Framework for LLM Quantization
2025Jinhao Zhang, Yunquan Zhang et al.
[2]
Rethinking Parameter Sharing as Graph Coloring for Structured Compression
2025Boyang Zhang, Daning Cheng et al.
[3]
MoQE: Improve Quantization Model performance via Mixture of Quantization Experts
2025Jinhao Zhang, Yunquan Zhang et al.
[4]
Compression for Better: A General and Stable Lossless Compression Framework
2024Boyang Zhang, Daning Cheng et al.
[5]
SpinQuant: LLM quantization with learned rotations
2024Zechun Liu, Changsheng Zhao et al.
[6]
PTMQ: Post-training Multi-Bit Quantization of Neural Networks
2024Ke Xu, Zhongcheng Li et al.
[7]
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
2023Wenqi Shao, Mengzhao Chen et al.
[8]
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
2023Jerry Chee, Yaohui Cai et al.
[9]
AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration
2023Ji Lin, Jiaming Tang et al.
[10]
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
2022Guangxuan Xiao, Ji Lin et al.
[11]
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
2022Elias Frantar, Saleh Ashkboos et al.
[12]
Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance
2022Chen Tang, Kai Ouyang et al.
[13]
Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization
2021Weihan Chen, Peisong Wang et al.
[14]
HAWQ-V3: Dyadic Neural Network Quantization
2021
[15]
Up or Down? Adaptive Rounding for Post-Training Quantization
2020Markus Nagel, Rana Ali Amjad et al.
[16]
ZeroQ: A Novel Zero Shot Quantization Framework
2020Yaohui Cai, Z. Yao et al.
[17]
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
2019Zhen Dong, Z. Yao et al.
[18]
AutoQ: Automated Kernel-Wise Neural Network Quantization
2019Qian Lou, Feng Guo et al.
[19]
HAQ: Hardware-Aware Automated Quantization With Mixed Precision
2018Kuan Wang, Zhijian Liu et al.
[20]
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
2018Bichen Wu, Yanghan Wang et al.

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"HeRo-Q offers a robust, easy-to-integrate algorithm that enhances the stability of low bit quantization in large language models."

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